Sampling Frequency is the pace at which measurements are taken of a continuous signal, expressed as samples per unit of time; a higher sampling frequency allows a more faithful reconstruction of the original signal.
In signal processing, sampling is the reduction of an analog signal, like a voice, into a stream of numbers representing the signal. Sampling frequency is usually measured in samples per second, or hertz. A lower sampling frequency results in a lower-quality reconstruction of the original signal, and a higher sampling frequency results in a higher-fidelity one. The Nyquist–Shannon sampling theorem sets the limit: a signal can be reconstructed only up to frequencies of half the sampling rate, and changes faster than that are misread, an error known as aliasing.
In 📝Weekly Accounting, the term carries over from electrical engineering to business data. Reading a company's numbers weekly rather than monthly or quarterly samples the business at a higher frequency, so changes show up while there is still time to act, and viewing the same metric at several sampling frequencies helps separate 📝Signal and Noise. The same idea describes how often investors receive data about the companies in their portfolios.
I came to this as an 📝Electrical Engineer. In digital signal processing you have to sample a signal often enough to see its shape; sample too slowly and you miss everything that matters. Monthly books are too slow a sample rate for a business.